Estimation of Longitudinal Speed of In-wheel Motor Driven Vehicle Using Fuzzy Extended Kalman Filter
-
摘要: 为了获取轮毂电机驱动车辆的纵向速度,设计了基于轮速信号和车身加速度信号的扩展卡尔曼滤波的估计算法,建立了研究对象的离散状态方程和测量方程,并采取不同的扩展卡尔曼滤波器对测量信号进行滤波去噪处理和车辆纵向速度估算.通过模糊控制器,对车速估计滤波器的估算参数进行实时动态调节,实现了估计算法的自适应性.研究结果表明,在路面附着系数为1.00的良好路面上,估算得到的车速和动力学模型输出的实际车速误差小于2%;在路面附着系数为0.25的路面上,最大估算误差小于10%.Abstract: In order to obtain the longitudinal speed of the in-wheel motor driven vehicle, a new estimation algorithm for the extended Kalman filter was designed based on signals of wheel speed and vehicle body acceleration. First, the discrete state equation and measurement equation of the research object were established. Then, two extended Kalman filters (EKFs), including a noise filter and an estimation filer, were designed to deal with measuring signals and estimate the vehicle's longitudinal speed, respectively. Finally, the parameters obtained by the estimation filer were adjusted through the fuzzy controller to ensure the adaptivity of the algorithm. The simulation results show that the error between the estimated speed and the actual speed was less than 2% when the road adhesion coefficient was 1.00, and the error was less than 10% when the road adhesion coefficient was 0.25.
-
GENG C, MOSTEFAI L, DENAI M, et al. Direct Yaw-moment control of an in-wheel motored electric vehicle based on body slip angle fuzzy observer[J]. IEEE/ASME Transaction on Mechatronics, 2009, 56(5): 1141-1419. HARTIKAINEN L, PETRY F, WESTERMANN S. Longitudinal wheel slip during ABS braking[J]. Vehicle System Dynamics, 2015, 53(2): 237-55. KO M G, JANG D Y, JOO J W, et al. Estimating the crash responses of a vehicle from the other size vehicle tested[J]. International Journal of Crashworthiness, 2015, 20(2): 165-76. 姜桂艳,常安德,李琦,等. 基于出租车GPS数据的路段平均速度估计模型[J]. 西南交通大学学报,2011,46(4): 638-644. JIANG Guiyan, CHANG Ande, LI Qi, et al. Estimation models for average speed of traffic flow based on GPS data of taxi[J]. Journal of Southwest Jiaotong University, 2011, 46(4): 638-644. LEVENBERG E. Estimating vehicle speed with embedded inertial sensors[J]. Transportation Research Part C: Emerging Technologies, 2014, 46: 300-308. ZHI Chenjiao, TANG Huiming. Vehicle speed detection method based on video corner feature matching[J]. Computer Engineering, 2013, 39(12): 176-80. 周聪,肖建,王嵩. 多采样率卡尔曼滤波器在汽车状态估计中的应用[J]. 西南交通大学学报,2012,47(5): 849-854. ZHOU Cong, XIAO Jian, WANG Song. Application of multirate unscented kalman filter to state estimation in vehicle's active front steering system[J]. Journal of Southwest Jiaotong University, 2012, 47(5): 849-854. KIM D H, CHOI K H, LI K J, et al. Performance of vehicle speed estimation using wireless sensor networks: a region-based approach[J]. Journal of Supercomputing, 2014, 71(6): 2101-2120. HODGSON D, MECROW B C, GADOUE S M, et al. Effect of vehicle mass changes on the accuracy of Kalman filter estimation of electric vehicle speed[J]. IET Electrical Systems in Transportation, 2013, 3(3): 67-78. CHEN Y, WANG J. Adaptive vehicle speed control with input injections for longitudinal motion independent road frictional condition estimation[J]. IEEE Trans. Veh. Technol., 2011, 60(3): 839-848. 陈瑶,李以农,韩家伟. 基于扩展卡尔曼滤波的轮毂电机驱动电动汽车状态估计[J]. 汽车工程学报,2015,5(1): 16-22. CHEN Yao, LI Yinong, HAN Jiawei. State estimation of in-wheel motor electric vehicle based on extended Kalman filter[J]. Chinese Journal of Automotive Engineering, 2015, 5(1): 16-22. ZHANG Xiangwen, XU Yong, PAN Ming, et al. A vehicle ABS adaptive sliding-mode control algorithm based on the vehicle velocity estimation and tyre/road friction coefficient estimations[J]. Vehicle System Dynamics, 2014, 52(4): 475-503. DI Xiang, HUANG Ying, GE Yanwu, et al. Fuzzy-PID speed control of diesel engine based on load estimation[J]. SAE International Journal of Engines, 2015, 8(4): 1669-1677.
点击查看大图
计量
- 文章访问数: 681
- HTML全文浏览量: 78
- PDF下载量: 404
- 被引次数: 0